• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.93-99
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• 2024

The digital medical imaging, especially, computed tomography (CT), should necessarily be considered in terms of noise distribution caused by converting to X-ray photon to digital imaging signal. Recently, the denoising technique based on deep learning architecture is increasingly used in the medical imaging field. Here, we evaluated noise reduction effect according to various noise types based on the U-net deep learning model in the lung CT images. The input data for deep learning was generated by applying Gaussian noise, Poisson noise, salt and pepper noise and speckle noise from the ground truth (GT) image. In particular, two types of Gaussian noise input data were applied with standard deviation values of 30 and 50. There are applied hyper-parameters, which were Adam as optimizer function, 100 as epochs, and 0.0001 as learning rate, respectively. To analyze the quantitative values, the mean square error (MSE), the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. According to the results, it was confirmed that the U-net model was effective for noise reduction all of the set conditions in this study. Especially, it showed the best performance in Gaussian noise.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.95-107
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• 1999

The design models of a new product in general are created using clay models or wooden mock-ups. The reverse engineering(RE) technology enables us to quickly create the CAD model of the new product by capturing the surface of the model using laser digitizers or coordinate measuring machines. Rapid prototyping (RP) is another technology that can reduce the product development time by fabricating the physical prototype of a part using a layered manufacturing technique. In reverse engineering process, however, the digitizer generates an enormous amount of point data, and it is time consuming and also inefficient to create surfaces out of these data. In addition, the surfacing operation takes a great deal of time and skill and becomes a bottleneck. In rapid prototyping, a faceted model called STL file has been the industry standard for providing the CAD input to RP machines. It approximates the CAD model of a part using many planar triangular patches and has drawbacks. A novel procedure that overcomes these problems and integrates RE with RP is proposed. Algorithms that drastically reduce the point clouds data have been developed. These methods will facilitate the use of reverse engineered geometric data for rapid prototyping, and thereby will contribute in reducing the product development time.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.9
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• pp.1106-1112
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• 1999

Substantial losses behind axial flow rotor are generated by the wake, various vortices in the hub region and the tip leakage vortex in the tip region. Particularly, the leakage vortex formed near blade tip is one of the main causes of the reduction of performance, generation of noise and aerodynamic vibration in downstream. In this study, the three-dimensional flow fields in an axial flow rotor were calculated with varying tip clearance under various flow rates, and the numerical results were compared with experimental ones. The numerical technique was based on SIMPLE algorithm using standard $k-{\varepsilon}$ model(WFM) and Launder & Sharma's Low Reynolds Number $k-{\varepsilon}$ model(LRN). Through calculations, the effects of tip clearance and attack angle on the 3-dimensional flow fileds behind a rotor and leakage flow/vortex were investigated. The presence of tip leakage vortex, loci of vortex center and its behavior behind the rotor for various tip clearances and attack angles was described well by calculation.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.2
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• pp.76-85
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• 1993

Based on the cutting force model, three-dimensional optimal design model was developed and optimal designed tool which is minimized cutting force is developed by computer simulation technique. In this model the objective function which is minimized resultant cutting force was used and the variables are radial rake angle, axial rake angle, lead angle of the tool. The cutting forces using conventional and optimal tools by simulation, are compared and analyzed in time and frequency domains. In time domain the cutting force of optimal tool in feed direction was more reduced and less fluctuated than that of conventional tool. Cutting forces of optimal tool in X-and Z-directions are shown a little increased than those of conventional tool. In frequency domain amplitude of insert frequency components of optimal tool in feed direction was more reduced than that of convent- ional tool. The amplitudes of insert frequency components of optimal tool in X-and Z-direction are a little increased than those of conventional tool. As the reduction of amplitude and fluctuations of the cutting force, Optimal tool is considered that tool life and surface roughness would be improved, and stable cutting would be expected.

• Computational Structural Engineering : An International Journal
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• v.3 no.1
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• pp.1-7
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• 2003

For system identification of large structures, it is not practical to identify the entire structure due to the prohibitive computational time and difficulty in numerical convergence. This paper explores the possibility of performing system identification at substructure level, taking advantage of reduction in both the number of unknowns and the number of degrees of freedom involved. Another advantage is that different portions (substructures) of a structural system can be identified independently and even concurrently with parallel computing. Two substructural identification methods are formulated on the basis whether substructural approach is used to obtain first-order or second-order model. For substructural first-order model, identification at the substructure level will be performed by means of the Observer/Kalman filter Identification (OKID) and the Eigensystem Realization Algorithm (ERA) whereas identification at the global level will be performed to obtain second-order model in order to evaluate the system's stiffness and mass parameters. In the case of substructural second-order model, identification will be performed at the substructure level throughout the identification process. The efficiency of the proposed technique is shown by numerical examples for multi-storey shear buildings subjected to random forces, taking into consideration the effects of noisy measurement data. The results indicate that both the proposed methods are effective and efficient for damage identification of large structures.

• Phonetics and Speech Sciences
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• v.7 no.3
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• pp.131-138
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• 2015

In this paper, we propose L1-norm regularization for state vector adaptation of subspace Gaussian mixture model (SGMM). When you design a speaker adaptation system with GMM-HMM acoustic model, MAP is the most typical technique to be considered. However, in MAP adaptation procedure, large number of parameters should be updated simultaneously. We can adopt sparse adaptation such as L1-norm regularization or sparse MAP to cope with that, but the performance of sparse adaptation is not good as MAP adaptation. However, SGMM does not suffer a lot from sparse adaptation as GMM-HMM because each Gaussian mean vector in SGMM is defined as a weighted sum of basis vectors, which is much robust to the fluctuation of parameters. Since there are only a few adaptation techniques appropriate for SGMM, our proposed method could be powerful especially when the number of adaptation data is limited. Experimental results show that error reduction rate of the proposed method is better than the result of MAP adaptation of SGMM, even with small adaptation data.

• Journal of Welding and Joining
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• v.25 no.4
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• pp.79-85
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• 2007

The industrial disaster caused by a work-related disease like a Musculoskeletal Disorders(MSDs) becomes a big social problem and increases rapidly. This leads to the degradation of the labor desire and the productivity. Welding work belongs to the work with a high intensity. This paper aims to analyze the welding work in the various positions from a view-point of the burden of the human musculoskeletal system and to propose the desired position with lower burden. For this purpose the real welding work was observed in the shipyard and analyzed using the RULA method, a powerful ergonomics tool. The 3-dimensional simulation model fur this work was also developed. In this model, ergonomics human model and welding work environment were built. This model was verified through the comparison to the real work. This paper showed that the improvement of welding position by changing the location of a stool and using some auxiliary tool can reduce the work intensity remarkably and lead to the decrease of MSDs.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.3
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• pp.39-49
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• 2003

The delta operator approach and the singular perturbation technique are introduced. The former reduces the round-off error in the numerical computation. The latter reduces computing time by decoupling the original system into the fast and slow sub-systems. The aircraft dynamics consists of the Phugoid and short-period motions whether its model is longitudinal or lateral. In this paper, an approximated solutions of lateral dynamic model of Beaver obtained by using those two methods in compared with the exact solution. For open-loop system and closed-loop system, and approximated solution gets identical to the exact solution with only one iteration and without iteration, respectively. Therefore, it is shown that implementing those approaches is very effective in the flight dynamic and control.

• Journal of Power Electronics
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• v.19 no.2
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• pp.403-412
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• 2019

Aiming at the problems of large dv/dt and di/dt in traditional single-ended converters and high electromagnetic interference (EMI) noise levels, a single-ended isolated converter using the secondary resonance technique is proposed in this paper. In the proposed converter, the voltage stress of the main power switch can be reduced and the voltage across the output diode is clamped to the output voltage when compared to the conventional flyback converter. In addition, the peak current stress through the main power switch can be decreased and zero current switching (ZCS) of the output diode can be achieved through the resonance technique. Moreover, the EMI noise coupling path and an equivalent model of the proposed converter topology are presented through the operational principle of the proposed converter. Analysis results indicate that the common mode (CM) EMI noise and the differential mode (DM) EMI noise of such a converter are deduced since the frequency spectra of the equivalent controlled voltage sources and controlled current source are decreased when compared with the traditional flyback converter. Furthermore, appropriate parameter selection of the resonant circuit network can increase the equivalent impedance in the EMI coupling path in the low frequency range, which further reduces the common mode interference. Finally, a simulation model and a 60W experimental prototype of the proposed converter are built and tested. Experimental results verify the theoretical analysis.

• Korean Journal of Optics and Photonics
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• v.34 no.5
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• pp.192-201
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• 2023

The band-limited angular spectrum method (BL-ASM) causes aliasing errors due to spatial frequency control problems. In this paper, a sampling interval adjustment technique for phase holograms and a technique for reducing speckle noise and improving image quality using a deep-learningbased U-net model are proposed. With the proposed technique, speckle noise is reduced by first calculating the sampling factor and controlling the spatial frequency by adjusting the sampling interval so that aliasing errors can be removed in a wide range of propagation. The next step is to improve the quality of the reconstructed image by learning the phase hologram to which the deep learning model is applied. In the S/W simulation of various sample images, it was confirmed that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were improved by 5% and 0.14% on average, compared with the existing BL-ASM.